• Corpus ID: 204896649

INTEGRATING NONLINEAR DECISION FUNCTIONS WITH PRINCIPAL COMPONENT ANALYSIS IN FMRI LANGUAGE ACTIVATION PATTERNS CLASSIFICATION

@inproceedings{Adjouadi2009INTEGRATINGND,
  title={INTEGRATING NONLINEAR DECISION FUNCTIONS WITH PRINCIPAL COMPONENT ANALYSIS IN FMRI LANGUAGE ACTIVATION PATTERNS CLASSIFICATION},
  author={Malek Adjouadi and Xiaozhen You and Magno R. Guillen and Melvin Ayala and Mercedes Cabrerizo and Prasanna Jayakar and A. Barreto and N. Rishe and Joseph E Sullivan and Dennis J. Dlugos and Madison M. Berl and John W. VanMeter and Drew Morris and Elizabeth J Donner and Bruce Bjornson and M. Smith and Byron Bernal and William Davis Gaillard},
  year={2009}
}
Epilepsia, 50(Suppl. 11):1–502, 2009 doi: 10.1111/j.1528-1167.2009.02377.x two distinct activation patterns among the 122 real datasets were identified as illustrated in Figure 1. In order to assess the significance of these groupings, the results were compared with those obtained using clinical rating and lateralization index (LI). Good agreements were found for both: 82.79% agreement with LI (Kappa 0.592) and 81.15% agreement with visual rating (Kappa 0.548). Conclusions: The data-driven… 
2 Citations
Principal component analysis and assessment of language network activation patterns in pediatric epilepsy
TLDR
This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI).
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